Abstract. To facilitate the construction of a satellite-derived 2 m air temperature (T2 m) product for the snow- and ice-covered regions in the Arctic, observations from weather stations are used to quantify the relationship between the T2 m and skin temperature (Tskin). Multiyear data records of simultaneous Tskin and T2 m from 29 different in situ sites have been analysed for five regions, covering the lower and upper ablation zone and the accumulation zone of the Greenland Ice Sheet (GrIS), sea ice in the Arctic Ocean, and seasonal snow-covered land in northern Alaska. The diurnal and seasonal temperature variabilities and the impacts from clouds and wind on the T2 m–Tskin differences are quantified. Tskin is often (85 % of the time, all sites weighted equally) lower than T2 m, with the largest differences occurring when the temperatures are well below 0 ∘C or when the surface is melting. Considering all regions, T2 m is on average 0.65–2.65 ∘C higher than Tskin, with the largest differences for the lower ablation area and smallest differences for the seasonal snow-covered sites. A negative net surface radiation balance generally cools the surface with respect to the atmosphere, resulting in a surface-driven surface air temperature inversion. However, Tskin and T2 m are often highly correlated, and the two temperatures can be almost identical (<0.5 ∘C difference), with the smallest T2–Tskin differences around noon and early afternoon during spring, autumn and summer during non-melting conditions. In general, the inversion strength increases with decreasing wind speeds, but for the sites on the GrIS the maximum inversion occurs at wind speeds of about 5 m s−1 due to the katabatic winds. Clouds tend to reduce the vertical temperature gradient, by warming the surface, resulting in a mean overcast T2 m–Tskin difference ranging from −0.08 to 1.63 ∘C, with the largest differences for the sites in the low-ablation zone and the smallest differences for the seasonal snow-covered sites. To assess the effect of using cloud-limited infrared satellite observations, the influence of clouds on temporally averaged Tskin has been studied by comparing averaged clear-sky Tskin with averaged all-sky Tskin. To this end, we test three different temporal averaging windows: 24 h, 72 h and 1 month. The largest clear-sky biases are generally found when 1-month averages are used and the smallest clear-sky biases are found for 24 h. In most cases, all-sky averages are warmer than clear-sky averages, with the smallest bias during summer when the Tskin range is smallest.

The Optimal Estimation (OE) technique is developed within the European Space AgencyClimate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA's Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST.

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